Automatic Recognition of Personality from Digital Annotations
Nizar Omheni
, Anis Kalboussi
, Omar Mazhoud
and Ahmed Hadj Kacem
Higher Institute of Computer and Management, University of Kairouan, Kairouan, Tunisia
Faculty of Economics and Management, University of Sfax, Road of the Airport Km4 3018, Sfax, Tunisia
Keywords: Learning User Profile, Data Extraction, Annotations, Digital Annotations, Personality Traits.
Abstract: There is an increasing interest in understanding human perception based on reading and writing behaviours.
Such researches are interested to seek knowledge of an individual’s personality as a way to predict their
behaviours and preferences across different contexts and environments. Recent works show significant
relation between the reader personality and his reading behaviours. Based on these findings, annotation
activity is considered as potential source to predict certain personality traits of readers. In this paper, we take
advantage of such theoretical works and we propose an online environment of active reading used to
explore practically the utility of annotation in reflecting an accurate user personality profile. We apply the
paired t-test to evaluate the system’s efficiency to measure human traits versus the scores of personality
traits measured using the Neo-IPIP inventory. Our findings show plainly that some traits of users’
personalities can be predicted accurately from digital annotation traces during online reading session.
The psychological researches show that personality
traits are consistently stable over time and constitute
a significant inter-individual difference (Burger,
2011; Cobb-Clark and Schurer, 2012). This stability
is considered as the key-assumption of personality
psychology which has the aim to predict observable
individual differences based on human traits
characteristics that is measurable in quantitative
terms (Matthews et al., 2009). The findings of
personality psychology area interested increasingly
the computing community which leads to the growth
in number of research paper in the topic of
personality computing (Vinciarelli and Mohammadi,
Many scholars are motivated to model users’
traits and they are interested to seek knowledge of an
individual’s personality as a way to predict their
behaviours and preferences across different contexts
and environments (Bologna et al., 2013; Selfhout et
al., 2010).
To assess user’s personality traits, several works
control user’s behavioural residues traces in the
digital environments (Kosinski et al., 2013). For
instance, (Bachrach et al., 2012; Golbeck et al.,
2011; Quercia et al., 2011) have analyzed the
relationship between personality and users’
behaviours in on-line social environments. The
works’ findings show that users’ personalities can be
accurately predicted through their traces in social
There is an increasing interest in understanding
human perception based on features extracted from
reading and writing behaviours. To show the ability
to profile user personality from human text
production and peculiarities of reading behaviours,
many researchers study the relation between user’s
personality traits and several factors such as text
(Wright and Chin, 2014; Celli, 2012; Arya et al.,
2012), linguistic cues (Celli and Poesio, 2014;
Mairesse et al., 2007; Celli, 2012a), handwriting
styles (Rahiman et al., 2013; Prasad et al., 2010;
Fisher et al., 2012), posts written in online social
spaces (Iacobelli et al., 2011; Sumner et al., 2012;
Qiu et al., 2012), social annotation (Mezghani et al.,
2012) and annotation traces during active reading
sessions (Omheni et al., 2014; Jackson, 2001).
The present work focuses on development of an
automated technique for determining the personality
traits of a user through analysis of digital annotation
traces in online reading environment.
The rest of this paper is as follows. In the next
section, we present an overview on related works.
Then, we propose an automated method to predict
accurately certain traits of users through their
annotation practices. Thereafter, we evaluate the
system’s performance to measure precisely the
Omheni N., Kalboussi A., Mazhoud O. and Kacem A..
Automatic Recognition of Personality from Digital Annotations.
DOI: 10.5220/0005483002730280
In Proceedings of the 11th International Conference on Web Information Systems and Technologies (WEBIST-2015), pages 273-280
ISBN: 978-989-758-106-9
2015 SCITEPRESS (Science and Technology Publications, Lda.)
scores of users’ traits. Finally, we draw some
conclusions and we cite certain possible directions
for future work.
The computing community is interested increasingly
to seek knowledge of an individual’s personality as a
way to predict their behaviours and preferences
across different contexts and environments
(Vinciarelli and Mohammadi, 2014).
The human personality is viewed from different
perspectives: biologically, psychoanalytically,
behaviourally, humanistically, cognitively, trait
perspective, etc. The most accepted model in
computing area is the trait perspective. Indeed, the
trait based model represents personality in terms of
numerical values which is a form particularly
suitable for computer processing (Vinciarelli and
Mohammadi, 2014).
Commonly, personality traits are assessed using
self-report techniques (Boyle and Helmes, 2009). In
computing area, the most popular technique used to
assess personality traits is the NEO-Personality-
Inventory, where the user rates his own behaviour
with Likert scales. For instance, (Nunes et al., 2008;
Nunes et al., 2008a) propose obtaining users’
personality information through their answers to the
NEO-IPIP inventory. The participators in the
authors’ experimentation were instructed to answer
900 questions. 10% of people answered all the
questions of the testing because they aren’t ready to
spend much effort for a long time to complete a
multi-item questionnaire. (Hu and Pu, 2010) use the
TIPI test (Ten-Item Personality Inventory)
developed by (Gosling et al., 2003) to acquire the
user’s personality characteristics. This inventory is
an extremely brief measure of the Big-Five
personality dimensions so the acquisition process
takes about 2-3 minutes to complete. Further
research works follow the same path to calculate the
user personality scores where each subject was
instructed to fill in a big five questionnaire (Tkalcic
et al., 2009; Wu et al., 2013).
Although the results shown in previous works is
fruitful we believe these researches have left certain
open issues concerning the followed approach to
obtain the information needed in the user modelling
process. Indeed, the crucial constraint in the
profiling process is to model a credible user’s profile
which reflects truly as much as possible the user in
the working environment. The explicit methods
require much from the user who is not ready,
usually, to fill long forms or even to write the truth
when completing forms about themselves
(Schiaffino and Amandi, 2009). Therefore, the main
limitation of self-assessments technique is that the
users might tend to bias the ratings towards socially
desirable characteristics knowing that the web-based
psychometric tests suffer of the control diminution
over the testing situation which lead to the high
probability of cheat especially in case where the
motivation to do is obvious and the personality
assessment can have negative consequences like,
e.g., failing a job inter
view (Vinciarelli and
Mohammadi, 2014; Barak et al., 2004; Gawronski
and De Houwer, 2014).
Certain psychologists seek to alternative
measurement instruments that reduce participants’
ability to control their responses and do not require
introspection for the assessment of psychological
attributes (Gawronski and De Houwer, 2014).
According to Brunswick’s lens model human
personality is externalized through distal cues
observable by others. These distal cues are
essentially physical traces left by individuals’
behaviours in virtually everything observable they
do (Vinciarelli and Mohammadi, 2014). In this
context, recent studies show the opportunity to
derive personality from digital traces of human
behaviours in different workspaces. Such works are
interested to show how users’ behaviours on Social
Networks relates to their personality, as measured by
the standard Five Factor Model (Kosinski et al.,
2013; Bachrach et al., 2012; Golbeck et al., 2011).
Other scientists study recognizing personality in
user’s speech and social interaction (Polzehl et al.,
2011; Ivanov et al., 2011).
Actually, in personality computing area, there is
a great interest to understand the human perception
based on reading and writing behaviours (Rahiman
et al., 2013; Wright and Chin, 2014; Minamikawa
and Yokoyama, 2011).
In the current essay, we are interested to a
specific behaviour of reading and writing activity:
The annotations. Indeed, we conducted a previous
work to show the relation between reader’s
personality and his annotation practices in “pen-and-
paper” context. The experiment showed an
interesting relation of correlation between certain
peculiarities of annotation activity and the
annotator’s personality traits (Omheni et al., 2014).
These findings motivate us to consider
the annotation traces in the digital context and to test
the possibility to recognize human’s personality
traits based on their digital annotations’ traces in
online reading environment.
Annotation is a handwritten practice which bridges
between reading and writing (Marshall, 1998) and
constitutes the most prominent habits of active
reading activity (Thomas, 2007).
(Kirwan, 2010, p. 5) considers the reader
marginalia (annotation) as: the “most direct,
reactionary response to the text that can feasibly be
considered” to study the relation between the reader
identity and the text. According to (Kirwan, 2010)
the annotations provide the link between reader, text,
and meaning and reflect the subjective individuality
of the annotator’s responses to the text. Based on
this subjective relationship, the author suggests
expanding the psychology-based reader theory to
include reader’s annotation practices.
The annotation activity is “a basic and often
unselfconscious way in which readers interacts with
texts” (Marshall, 2009, p. 38). Furthermore, the
annotation is described as a natural human activity
that is used in daily life as an integral part of reading
activity (O’hara and Sellen, 1997). Every annotator
has unique individual patterns in making annotations
(Naghsh, 2007). According to (Jackson, 2001, p. 5),
“if you ask annotators today what systems they use
for marking their books and where they learned
them, they generally tell you that their methods are
private and idiosyncratic”. Hence, the individuality
of annotation patterns shows us very plainly that
there can be some sort of connection between
annotation practices and annotator’s personality.
(Jackson, 2001) assumes that “marginalia
[annotation] express a reader’s impulsive and
unguarded reactions to a book” and she “consider[s]
them to be an exceptionally reliable guide to
personality” (Jackson, 2001, p. 87).
In our prior work (Omheni et al., 2014) we
conducted an empirical study to show the implicit
relation between reader’s annotation activity and his
personality traits. The study shows significant
correlations for Neuroticism, Conscientiousness, and
Extraversion traits. Furthermore, we made
predictions about a subject’s personality based on
multiple annotation features using the multivariate
linear regression method. Our findings show that
Neuroticism and Conscientiousness can be predicted
with reasonable accuracy, whereas other traits are
more difficult to be predicted.
Regarding the external validity of our findings,
first of all, in our study, we addressed the population
characterized as following: people aged between 18
and more, either man or woman, with different
occupations and interests and who practice
frequently the habit of reading and annotation of
textual materials. Hence, the size of population is so
large, which is in reality, not possible to sample the
whole population, due to budget, time and
feasibility. Thus, as a way to decide pragmatically
the generalization of our findings we made on the
basis of the selection of a sample group that is
representative of the target population. This is
something that we took into account when designing
our experiments. Our research design is governed by
the interest in the generalisability of our study’s
results. Hence, we do a good job of drawing a
representative sample from our addressed population
and we have not considered specific circumstances
of time and place in which the data were collected.
Indeed, in our study, we focus on the annotation
practice which is a ubiquitous human behaviour and
we have only considered the paper support of
annotated document. To measure the personalities
scores of volunteers we utilized the standard Five
Factor Model questionnaire (the NEO-IPIP
Inventory) which is the best accepted and most
commonly used scientific measure of human
personality traits (Peabody and De Raad, 2002). All
the tools and circumstances taken into account to
achieve our experiment can be considered in a
variety of places, with different people and at
different times. Thus, based on logical
considerations and speculation concerning the extent
to which our sample is similar to the target
population and the replication of our experiments
with other representative groups in other locations
can strongly give the same results and findings, we
ensure that we can generalize our findings to the
entire population in our study.
Based on what previously cited, it is plain, that a
reader’s annotation activity is really an expression of
his personality traits. Indeed, we show very plainly
that the considered features descriptive of the
annotation practices in our prior study may appear
insignificant in themselves, but, they are
nevertheless all very significant as indications of the
annotator’s personality traits (Omheni et al., 2014).
Recent researches endeavour to replace the “pen-
and-paper” paradigm for the annotation needs by
employing the technology of free form digital ink
annotations which add the flexibility and natural
expressiveness of the traditional handwriting method
to the digital annotation process (Kalboussi et al.,
2015). Such tools enable readers to annotate their
digital documents similarly to “pen-and-paper” case.
For instance, iAnnotate (Plimmer et al., 2010) is an
annotation tool for android system which enables
users to add annotations with the pencil, highlighter,
and note tools to their digital texts. Hence, the digital
context of free form annotation process is very
similar to the context of pen-and paper. The high
degree of proximal similarity among these two
contexts constitutes a strong evidence to generalize
our study’s results (Omheni et al., 2014) to digital
annotation environment. Thus, we are motivated to
take advantage of digital annotations which can be
considered as a source of knowledge to
automatically predict an annotator’s personality
In this subpart of our study, we focus on
development of an automated technique for
determining the characteristic traits of a person
through the features extracted from his annotation
activity. In fact, the proposed system “i-Read” is an
online reading environment where the user can
upload their reading materials and practice their
annotation habit.
The following figure (fig.1) illustrates the
interaction between the various modules of “i-Read”
system along with the flow of information/data. The
proposed architecture consists of user annotation
interface, the annotation analyzer module, the
constructor profile module and three databases with
two servers.
Figure 1: The Architecture of “i-Read” Online Reading
To avoid destroying the original version of
reading materials, our system uses an independent
annotation database, which differs from the
documents database, to store annotated information
and contexts from readers. Moreover, the annotation
interface provides several powerful annotation
functionalities, such as scribbling, highlighting,
underlining, commenting, as a way to engage users
actively with their reading materials.
4.1 The Annotation Analyser Module
To predict the actual personality traits of the
individual we consider the features studied in
(Omheni et al., 2014) to be extracted automatically
by the module of annotation analysis. To extract the
considered features we start, first of all, by
classifying annotations in three general categories.
This categorization is based on how annotations can
appear and be represented. (Agosti and Ferro, 2003)
define three ways to represent the meaning of
1. Textual annotation expressed by a piece of
text added to the annotated document,
2. Graphic annotation expressed by a graphic
mark added to a document,
3. Reference annotation expressed by a link
between two texts or two textual pieces in the
same document.
The authors called these basic ways “signs of
annotation” and they define the term sign as a
formation of a meaning. Furthermore, according to
(Agosti and Ferro, 2003), these signs can be
combined together to express more complex signs of
annotation. In current work we try to quantify the
user’s digital annotation traces by collecting a set of
statistics describing the total number of annotations,
average number of annotations per page of reading
material, number of textual annotations, number of
graphical annotations, and number of referential
4.2 The Constructor Profile Module
In our empirical study, we used the multiple linear
regression analysis to assess the association between
six independent variables representing the different
features qualifying the annotation activity and a
single continuous dependent variable represents the
focused user’s trait. The multiple linear regression
equation is as follows:
Y = b
+ b
+ b
+ b
+ b
+ b
where Y is the predicted or expected value of the
dependent variable representing the score of the
focused user’s personality trait, X
through X
are the
distinct independent or predictor variables, b
is the
value of Y when all of the independent variables (X
through X
) are equal to zero, and b
through b
the estimated regression coefficients. Based on this
mean function, we can determine the expected
annotator’s personality trait as long as we know
certain peculiarities characterizing quantitatively her
annotation practices.
As we cited previously, based on the similarity
between the two contexts of annotation process (the
manual and digital contexts) we have generalized
our study’s results to the digital environment. Thus,
in current work, we are motivated to apply the
study’s findings (Omheni et al., 2014). The scenario
of the proposed automatic personality profiling is as
follow: first of all the annotation analyzer module
captures certain features which tend to characterize
quantitatively the reader’s annotation practices. The
collected data is used thereafter as a source of
knowledge to extract the implicit information which
describes the personality of active user. Indeed, the
quantitatively information is transferred to the
constructor profile module as an input data to the
equations used to estimate the scores of user traits
4.3 System Operation Procedure
Based on the system architecture (fig.1), the system
operating procedure is described and summarized as
1. A user uploads his/her reading document on
the “i-Read” online environment;
2. The system saves the document in the
documents repository;
3. The user annotates his/her reading material;
4. The system saves the user’s annotations in
the Annotations repository;
5. The annotation analyzer module captures the
user’s annotation activity and extracts such
6. The annotation analyzer module sends the
collected information to the profile
constructor module to build the user
personality traits profile;
7. The profile constructor module considers the
collected information as an input data to the
equations used to estimate the scores of user
traits profile;
8. The system saves the modelled user’s profile
in the Profiles repository.
4.4 System’s Performance Evaluation
In order to evaluate the system’s level of
performance in measuring accurately the scores of
reader’s personality traits, we conducted the
following experiment and we invited 100 volunteers.
The invited people have the same characteristics
qualifying the target population in our prior work
(age, gender, habit of reading and annotation).
To assess whether our system measures accurately
the user’s traits, we invited the participators to
upload their textual materials on the “i-Read
environment and we instructed them to use the
system to achieve their reading and annotation
activities (fig.2). Next, they were instructed to
answer a standard Five Factor Model questionnaire
(the NEO-IPIP Inventory) to obtain a feedback
regarding their personality based on their responses.
Figure 2: Annotated document opened in “i-Read” online
To show the system’s efficiency to measure
accurately the scores of reader’s conscientiousness
and neuroticism traits compared to the values
determined using the NEO-IPIP Inventory, we
applied the paired t-test to compare the scores of
certain user’s personality traits obtained through the
two different methods of measurement. We look to
determine whether there is a significant difference
between the paired values of scores. Both
measurements are made on each subject in the
selected sample, and the test is based on the paired
differences between these two values. The test
statistic is calculated as following:
t =
/n (2)
is the mean difference, s
is the sample variance, n
is the sample size and t is a Student t quantile with
n-1 degrees of freedom. In our case n = 100. Tables
3 and 4 show descriptive statistics of t-test measure
of the difference significance between the paired
values of user’s conscientiousness and neuroticism
traits scores measured with two different systems:
the “i-Read” system and the Neo-IPIP inventory.
Analytical results indicate that the scores of user’s
Conscientiousness and Neuroticism characteristics
obtained through the “i-Read” system did not differ
significantly (Sig1 = 0,72 > 0.05; Sig2 = 0,53 >
0.05) versus the scores measured using the Neo-IPIP
inventory (Table 1. and 2.). Thus, the experimental
results show the possibility to measure some
personality traits (Conscientiousness and
Neuroticism) with reasonable accuracy by reference
to reader’s digital annotation practices.
Table 1: A t-test measure of the difference significance
between the paired values of Conscientiousness scores
measured with two different systems.
Mean Std.Dv. t-value p-value
25,78 4,90
26,50 20,25 -0,36 0,72
Table 2: A t-test measure of the difference significance
between the paired values of Neuroticism scores measured
with two different systems.
Mean Std.Dv. t-value p-value
64,66 6,74
63,37 21,16 0,63 0,53
These results is coherent to our prior findings
(Omheni et al., 2014) and support the hypothesis of
the existence of some sort of connection between
annotation traces and certain personality traits of the
annotator. These discoveries are promising and
constitute a new tendency in modelling human traits
by reference to certain behavioural residues of
reading and writing activities.
In this paper, an automated method has been
developed to predict certain personality
characteristics of a person from features extracted
through his annotation behaviours. We have proven
that some traits of users’ personalities can be
predicted accurately from digital annotation traces in
online reading environment. This shows us very
convincingly that there is some sort of connection
between the peculiarities of annotation activity and
certain personality traits of the annotator.
We want to enhance the proposed system to be
used in personality-based systems. Another future
direction, we want to increase the features used to
derive user’s personality traits based on their
annotation practices for more correct results. These
investigations can be subjects of follow-up works in
the near future.
The current work can be complementary to the
prior works of (Kalboussi et al., 2014; Kalboussi et
al., 2013; Kalboussi et al., 2013a; Kalboussi et al.,
2013b) which aim to invoke the web services based
on annotation activity of a reader. Thus, our work
can be useful in the adaptation process of the
invoked web services. As a summary, we can
consider the modelling process of reader's
personality based on annotation traces is a step
forward in the area of personalization over web.
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